MLOps or Machine Learning Operations are a set of best practices for machine learning development cycles. Like DevOps, MLOps is also a discipline that deals with the continuous flow of deliverables. Machine learning operationalization allows machine learning models to be delivered with efficient workflows that are iterative.
MLOps is used for the production and deployment of most machine learning applications that are out there. MLOps takes inspiration from CI/CD (continuous integration/continuous deployment) practices and beenfits in terms of defining or managing lifecycles to retrain and update working ML models.
MLOps also helps increase the scalability of production and continually deliver high-performing or powerful ML models as scheduled. Through continuous testing and training, ML models can keep getting improved without compromising the original build.
Developers, data scientists, data architects, data engineers, and ML engineers must all adhere to MLOps best practices to design, manage, build, and deploy models as efficiently as possible during production. MLOps also helps secure ML models and automate development pipelines. Automated ML development pipelines or ML pipelines help in maximizing the production performance and increasing the return on investment (ROI).
MLOps also promotes monitoring and ensures that security and compliance standards are adhered to. Infrastructure management, model version control, model serving, and model pipelining are all essential parts of MLOps.
Model retraining is also an essential component of MLOps. As a matter of fact, continual model retraining is one of the essential MLOps best practices. Automated model retraining allows projects to benefit from continuous training and testing, a preferred practice in the domains of both data science and artificial intelligence.
After tracking and monitoring the performance of your ML model, the next phase involves improving the model. How? Through model retraining.
Model retraining ensures that the quality of your ML model does not decrease with time and that the data your model is based on is up-to-date. Retraining models is an essential part of ML model management.
Retraining models on new data prevents projects from becoming irrelevant and allows models to keep up with the times (or changes in the environment). For instance, the training data that has been used to develop an ML model might be insufficient to work with input that it cannot recognize. Thus, training data must keep getting updated and enhanced in order to keep improving ML models.
Now, once a model is deployed, it cannot be stopped or taken down just to dissect and modify it. Thus, the best practices of model retraining are essential in order to keep running the working model while safely retraining the model.
Without continuous testing and training properly, it can lead to a crash (due to unscheduled sudden modifications). Or, it can lead to the model getting outdated if there is no retraining at all. All organizations and individuals must set up a model retraining pipeline when working with various applications of ML such as a predictive analytics system or a recommendation engine.
During model retraining in machine learning, model algorithms, model features, and hyperparameter search spaces all remain unchanged, thus, generally, not requiring data scientists, ML engineers, and developers to make any code changes. Unless any code needs to be changed to adapt to particular data types, there is no need to manipulate the architecture of the ML model.
So, model retraining is simply re-running the entire process that created the current working model. However, rather than the old training data, a new and updated set of training data is used. MLOps is agnostic towards programming languages, base platforms, IT infrastructure, and frameworks. However, training data must have a certain degree of similarity to function in a similar manner to its predecessor.
For example, to retrain models with new data, Python, C++, Ruby, Scala, and any other programming language can be used. Similarly, any framework and library such as TensorFlow, Pandas, Keras, Scikit-learn can be adapted into MLOps.
The Importance of Model Retraining
ML models should be frequently retrained, especially with the heavy alterations in external and internal factors that can lead to the model producing inaccurate output. Outdated models produce results that might even be irrelevant to the current environment.
For instance, a search engine algorithm would need continual retraining to keep up with the latest search trends and trending topics. The code for these models does not change, but due to being retrained on new data, the accuracy and relevancy of the model’s output simply become enhanced. Similarly, in a fraud detection algorithm, current statistics and fraudulent trends are analyzed and taken into account by the ML model to identify fraudulent schemes and behavior.
Training, testing, and then deploying are not the only responsibilities of data scientists and ML engineers. Model retraining, adhering to product lifecycles, and continually improving ML products are also essential job responsibilities of ML developers.
Assuming that an ML model will keep working forever is a big mistake as with new data getting generated every single day, a model is sure to get outdated within a few weeks, if not days. Advanced ML lifecycles retrain their models in a distributed manner through collaborative workflows that involve frequently and parallelly training the model in order to increase development efficiency.
ML models that are deployed in production are unable to adapt to various changes in data themselves, regardless of how advanced they are. Human supervision is crucial for model retraining, at least for now. Unpredictable events such as pandemics, famine, and economic collapse completely alter the validity of models trained on data that was acquired before an unexpected event.
Benefits of MLOps
The benefits of MLOps include:
- MLOps helps in unifying the release and production lifecycle of ML products.
- Agile principles and CI/CD best practices can be adhered to with MLOps.
- MLOps promotes automated model testing, automated ML model integration testing, and automated data validation.
- Automated testing and retraining can lead to the reusability of code while reducing technical debt and development time.
Different Approaches to Retraining Schedules
Before planning the model retraining schedule for a project, it is mandatory to first understand the business or product requirement. Some models need frequent retraining while some need training based on various triggers. Let us learn about the four main approaches for choosing a retraining schedule.
- Retraining based on interval: This is a form of periodic training that is scheduled on a daily, weekly, monthly, or yearly basis. These kinds of retraining schedules give you a better understanding of when the ML model’s training data gets updated.
- Performance-based trigger: This kind of retraining approach is used based on the performance of the current working model. First, there is a baseline metric score that is determined after deployment of the model and then a trigger is built to monitor if the model’s performance falls below the set standard. Once the model shows degradation, the retraining schedule is triggered.
- Trigger-based on data changes: This kind of retraining is triggered by any changes made in the flow of data. Data pipelines are essential for the ML models to adapt to dynamic environments. If you monitor the upstream data flowing in production, you can find out if there are heavy changes in the distribution of data.
- Retraining on demand: This is the most manual approach to retraining ML models. Retraining on demand involves employing various traditional tools useful for retraining existing models. These retraining approaches are not automated and are generally used for small projects or startups.
Data Volume Needed for Retraining
Sustaining an ML model is said to be harder than actually building a model from scratch. This is due to multiple factors such as data distribution and dependencies.
It is hard to truly determine how much data is required to retrain a model effectively. If there is an ample amount of historical data available, ML models can learn the dependencies between sets of independent features and target variables. Now, in order to reduce prediction errors, the dependency that is learned in the most effective way by the model is calculated on the basis of evaluation metrics.
When we are working with a model that is deployed in production and we wish to retain the accuracy of this model, minor readjustments to the data are necessary. Then, what are the adjustments that are not recommended?
Well, in order to make the model function like its predecessor, it is best that the new retraining dataset also belongs to similar distributions. The observed data in both the datasets (original dataset and retraining dataset) should possess similar types of data.
Now, when we talk about similar data, it is a very vague topic as different machine learning models with different algorithms process their training data in completely different ways. Thus, in order to not allow retraining data to deviate from previous training data, we must identify metrics that allow us to limit the data.
When we limit the deviation of data, we can focus on certain aspects of the data and increase or modify that section. An example is not altering large metrics such as sub-categories but instead adding in new data for the main data type. When we think about data in terms of rows or columns (relational databases, it can be just an increase in the number of rows (particulars) rather than more metrics (columns) being introduced.
There is no single solution for understanding how much data is needed for retraining. The only answer here is that the amount of data is irrelevant as long as the distribution and complexity of data match the ML model’s base training data.
The Two Different Retraining Algorithms
When talking about how to retrain a model with new data, we must cover the two main retraining algorithms that are used.
- Continual Learning: This learning methodology is known as lifelong learning and this mimics how humans learn things. This kind of algorithm initiates an ML product lifecycle that keeps making consistent alterations over time in order to learn and improve as new data is made available. For example, a change in the behavior of a market or end-user would lead the ML model to make the necessary adjustments.
- Transfer Learning: This is a learning methodology that retrains ML models based on the existing model, rather than getting triggered by new data. Transfer learning depends on the existing model’s output to improvise and consistently upgrade a new ML model. This kind of learning algorithm improves the ML model incrementally by self-optimization.
The advantages of continual learning are:
- Allows us to save training time
- Helps in retaining knowledge after training
- Enables models to become auto-adaptive
- Improves the performance of models
The limitations of continual learning are:
- Susceptibility to concept drift
- Chances of deviation in new datasets
The advantages of transfer learning include:
- Knowledge can be transferred
- Models can be trained based on new concepts and for new tasks
- Helps us save time in retraining by not requiring us to train the model from scratch
The disadvantages of transfer learning include:
- Susceptibility to data drift
- Only functions properly when the base model is proportional to the current requirements
- Initial problems become irrelevant eventually
Like DevOps is essential for software development lifecycles, MLOps is extremely crucial for machine learning product lifecycles. Without adhering to MLOps, one cannot expect a machine learning model to be successful in production. Similarly, without retraining, machine learning models cannot improvepost-deployment.